论文标题
Covid-19的有效社会距离：经济健康与公共卫生的融合
Efficient Social Distancing for COVID-19: An Integration of Economic Health and Public Health
论文作者
论文摘要
在药物治疗可用性之前，社会距离一直是控制传染病的唯一有效方法。它可以以经济成本降低疾病的感染率。然而，像Covid-19这样的大流行危机对决策者构成了困境，因为长期限制性的社会疏远甚至封锁将使经济成本上升。本文使用具有移动性控制的随机流行病建模来研究Covid-19的经济健康和公共卫生问题的综合风险，以管理有效的社会疏远政策。社会距离是限制社区流动性，最近可以通过大数据分析来访问。本文利用社区流动性数据对COVID-19流程进行建模，并从主要市场指数价格推断出COVID-19驱动的经济价值，这使我们能够制定对有效的社会疏远政策作为随机控制问题的搜索。我们建议通过深入学习方法解决问题。通过将我们的框架应用于美国数据，我们从经验上研究了美国社会疏远政策的效率，并提供了算法产生的建议。
Social distancing has been the only effective way to contain the spread of an infectious disease prior to the availability of the pharmaceutical treatment. It can lower the infection rate of the disease at the economic cost. A pandemic crisis like COVID-19, however, has posed a dilemma to the policymakers since a long-term restrictive social distancing or even lockdown will keep economic cost rising. This paper investigates an efficient social distancing policy to manage the integrated risk from economic health and public health issues for COVID-19 using a stochastic epidemic modeling with mobility controls. The social distancing is to restrict the community mobility, which was recently accessible with big data analytics. This paper takes advantage of the community mobility data to model the COVID-19 processes and infer the COVID-19 driven economic values from major market index price, which allow us to formulate the search of the efficient social distancing policy as a stochastic control problem. We propose to solve the problem with a deep-learning approach. By applying our framework to the US data, we empirically examine the efficiency of the US social distancing policy and offer recommendations generated from the algorithm.
